Abstract Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of
machine learning systems. For instance, in autonomous driving, we would like the driving …

Detecting out-of-distribution (OOD) samples is crucial for ensuring the safety of machine
learning systems and has shaped the field of OOD detection. Meanwhile, several other …

Abstract Out-of-distribution (OOD) detection is crucial for model reliability, as it identifies
samples from unknown classes and reduces errors due to unexpected inputs. Vision …

Out-of-distribution (OOD) detection aims to detect test samples outside the training category
space, which is an essential component in building reliable machine learning systems …

Zero-shot OOD detection is a task that detects OOD images during inference with only in-
distribution (ID) class names. Existing methods assume ID images contain a single, centered …

Detecting anomalies or out-of-distribution (OOD) samples is critical for maintaining the
reliability and trustworthiness of machine learning systems. Recently, Large Language …

Ensuring robust performance on long-tail examples is an important problem for many real-
world applications of machine learning, such as autonomous driving. This work focuses on …

Shifting deep learning models from lab environments to real-world settings entails preparing
them to handle unforeseen conditions, including the chance of encountering novel objects …

Trustworthy machine learning (TML) under imperfect data has recently brought much
attention in the data-centric fields of machine learning (ML) and artificial intelligence (AI) …

Open World Object Detection (OWOD) aims to adapt object detection to an open-world
environment, so as to detect unknown objects and learn knowledge incrementally. Existing …